由於視訊資料大量數位化，數位資料暴增，結合各種最新之壓縮及視訊處理標準，編碼及壓縮的目標不難達成，然而現階段最急於解決的是我們如何在最短的時間內找到使用者所感興趣的視訊片段，也就是說，我們要如何快速地、有效率地存取我們所要的數位資料嚴然成為一項重要的課題。因此，我們嘗試去建立一個能夠認知及分類視訊資料的系統模組，在這裡我們限定我們的應用範疇在運動節目的視訊資料，因為它們具有高重覆性的事件，高相關性的鏡頭特性，對我們在做分析時有較有利，例如籃球比賽的投籃事件大都屬於以下幾種：近距離投籃、中距離投籃、遠距離投籃和罰球。

我的論文主要是利用貝氏信度網路(Bayesian Belief Network)來當作影像處理所得到的低階特徵和高階語意之間的橋樑，經由貝氏信度網路可用來推論不易觀測到的高階語意，可用來作運動節目的語意認知及影片分類。首先這個系統會使用常見的影像處理方法，來擷取如顏色統計值、灰階值、邊緣…等，接著利用這些特徵當成SVT(Support Vector Tracking)演算法的輸入來追蹤球、投球者和籃球框，找出它們在空間和時間上彼此的相關性，當成影像處理的低階特徵，接著建構了一組貝氏信度網路來分類不同區域的投籃事件，同時也建構另外一組網路用來判斷投籃是否有得分。因此這個系統總共可以區分出八種不同的同投籃事件，包括：近距離投籃、中距離投籃、遠距離投籃和罰球，而這四種投籃又可判斷是否有得分。

The exploitation of semantic information from video is a nontrivial problem because of the large difference in representations, levels of knowledge and abstract episodes. Traditional image/video understanding, indexing is formulated in terms of low-level features describing image/video structure and intensity, while high-level knowledge such as common sense and human perceptual knowledge are encoded in abstract, non-geometric representations. In this thesis, we attempt to bridge this gap through the integration of image/video analysis algorithms with multi-level Bayesian Belief Network (BBN), and demonstrate how we can be effectively applied for fusing the evidence obtained from different video sources. Support vector tracking is applied for ball/shooter/basket tracking. SVM classifier and camera motion analysis combined with low–level feature extract algorithms are applied to extract mid-level features from the video which act as the input to the Bayesian Belief Network. We have proposed a novel video shot classification system based on low-level features extraction. Our semi-automatic semantic system is designated for the basketball game videos. Given the video shots of basketball game, our framework can identify four categories of shot event such as short shot, medium shot, long shot, free throw, and the score event. In the experiments, we demonstrate that our system may extract the low-level evidences and then interpret the high-level semantic of the video shot.

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